dialect divergence and convergence in new zealand...

Dialect divergence and convergence in New Zealand English

Molly Babel Department of Linguistics

University of British Columbia and University of California, Berkeley

INTRODUCTION

As people learn to speak, they acquire the language and dialect spoken around

them. The exact forms of every level of linguistic representation – from syntax to lexical

choice to pronunciation – are all determined by the patterns of the ambient language.

This is done with great ease as young language learners. It has been documented,

however, that after a certain age children are unable to fully acquire particularly complex

phonological aspects of a second dialect (Payne 1980, Chambers 1992). Trudgill

(1986:58) argues that this is due to the fact that accommodation of sound structure in

dialect contact is “phonetic rather than phonological” (emphasis in original).

Despite the purported difficulty in acquiring a new dialect, research on adults

exposed to new dialects demonstrates that ways of speaking do indeed change. For

example, Munro, Derwing, and Flege (1999) found that accents of Canadians living in

Alabama were perceived by listeners as sounding intermediate between those of

Canadians living in Canada and native Alabamans. A series of studies have focused on

longitudinal changes within the speech of a single speaker over a fifty year span.

Harrington (2006, 2007) and Harrington, Palethrope, and Watson (2000a, b) have

analyzed vowel changes in the speech of the Queen Elizabeth II. The recordings under

analysis were her yearly Christmas broadcasts from 1952 through 2002. After accounting

for maturational changes in the vocal tract, this work has demonstrated that the Queen’s

vowels shifted in the direction of Southern British English, away from Received

UC Berkeley Phonology Lab Annual Report (2009)

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Pronunciation. Evans and Iverson (2007) examined changes in both perception and

production in new dialect exposure. College students from a northern English dialect

(Sheffield) were interviewed at the commencement of university studies, three months

after the beginning of studies, after one year of study, and after two years of study at a

southern English university where Standard Southern British English (SSBE) was

spoken. SSBE has /ʌ/ in bud and cud and /a/ in bath. Northern varieties of English use the

vowel /ʊ/ for bud and cud and /ɑ/ for bath. The authors found that both bud and cud

became more centralized. In the northern dialects, cud and could are homophonous, both

having the vowel /ʊ/. Could has this vowel in the southern dialects, but cud has /ʌ/. After

their time at the university, the participants began to centralize the vowel for could as

well. Participants were also rated on a 10-point scale from ‘very northern’ to ‘very

southern’. Overall participants were rated as sounding more southern after their time at

the university. In terms of changes in perception, participants who were rated as having

maintained a more northern accent did in fact choose more northern perceptual

exemplars.

Delvaux and Soquet (2007) provide evidence demonstrating that speakers can

shift from one dialect to another after very brief periods of exposure. Female speakers of

the Mons dialect of Belgian French were exposed to a the voice of a female model talker

from the Liège dialect of Belgian French. After auditory exposure to the model talker’s

voice, the Mons speakers modified their pronunciations of /o/ and /ɛ/ in a sentence

production task.


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In addition to these findings that speakers acquire new dialect patterns as adults,

several laboratory-based studies have found that individuals acquire the phonetic

characteristics of within-dialect model talkers in speech tasks. In what is known as a

shadowing task or an auditory naming task where participants simply repeat single words

after a model talker, Goldinger (1998) found that when participants repeat words, they

acquire phonetic aspects of the model talker’s voice. This general finding has been well

replicated (Namy, Nygaard, & Sauerteig 2002, Goldinger & Azuma 2004). In the

completion of a conversational map task, speakers were also found to converge

phonetically on each other’s productions; the direction of the convergence though was

influenced by several social factors, namely gender and participant role (Pardo 2006). In

Pardo’s study, male dyads converged more than female dyads. As far as participant role,

women were found to converge toward the speaker who was receiving instructions while

men converged toward the speaker who was giving instructions. In the speech science

literature, there has also been some investigation of what exactly can be or is being

imitated. Shockley, Sabadini, and Fowler (2004) and Nielsen (2008) find that speakers

imitate the voice onset time of aspirated stops. Recently, Babel (2009) found that

phonetic accommodation of vowel formants is selective. In that study, the American

English vowels /i æ ɑ o u/ were examined for imitative behaviors; only /æ/ and /ɑ/

exhibited shifts toward the model talker in an auditory naming task.

There is ample evidence demonstrating that phonetic accommodation occurs in

language behavior. It is currently under great debate as to why this happens.

Psycholinguists who study the mechanisms of speech behavior have developed models to

account for accommodative behaviors in language. The interactive alignment model is a


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popular model proposed by Pickering and Garrod (2004) to account for speech

accommodation. In this model every level of linguistic representation – the situation

model, semantics, syntax, the lexicon, phonology, and phonetics – is connected within an

individual, and each level of representation between the listener and the talker is

connected. An automatic priming process that percolates through the levels of

representations of the interlocutors aligns speech. The use of a representation by a talker

leads to the activation of that representation in the listener, and that activation leads to

increased incidence of use.

Recently, sociolinguists have contributed to the discussion of why

accommodation happens. Traditionally, research on dialect contact and acquisition has

remained agnostic regarding the mechanisms and motivations for accommodation.

Trudgill (2008:252) argues that “accommodation is not only a subconscious but also a

deeply automatic process.” He reaches this conclusion after reviewing four cases of

European languages forming new varieties as a result of dialect contact and he discards

the theory that new dialects arise as a result of the formation of new national identities:

...if a common identity is promoted through language, then this

happens as a consequence of accommodation; it is not its driving

force. Identity is not a powerful enough driving force to account for

the emergence of new, mixed dialects by accommodation. It is

parasitic upon accommodation, and is chronologically subsequent to

it (Trudgill 2008:251).


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It is striking to see Trudgill eschewing social factors as a palpable force in

language change. Trudgill’s claim has been criticized extensively (Bauer 2008, Mufwene

2008, Tuten 2008, Schneider 2008, Coupland 2008, Holmes & Kerswill 2008). Bauer,

for example, points out there are instances where individuals have lived for a

considerable amount of time in a new dialect area without displaying significant

accommodation.

Trudgill’s hypothesis makes a prediction that is testable: if linguistic

accommodation is automatic, it should not only be demonstrable in a laboratory, but

everyone should do it. Some evidence may already exist to counter Trudgill’s claim.

Communication Accommodation Theory (Giles 1973, Giles & Coupland 1991, Giles,

Coupland, & Coupland 1991, Shepard, Giles, & LePoire 2001) has long argued that

language choices are a function of social distance. Linguistic convergence lessens the

social distance between interlocutors while divergence maximizes it. In particular, one

study poses a challenge for the automatic accommodation model. Bourhis and Giles

(1977) is the hallmark example of dialect divergence and served as inspiration for the

study presented in this paper. Bourhis and Giles examined accent convergence and

divergence in two groups of Welsh-born adults. A group of Welsh adults who attended

both Welsh language and Welsh culture classes were found to diverge from an out-group

speaker of Received Pronunciation (RP) by adopting a Welsh-accented dialect. The RP

interviewer had questioned the vitality and function of the Welsh language in modern

times. The second group of Welsh adults participating in the experiment also attended

Welsh language classes, but only on business time with the explicit goal of furthering

their careers. These adults were found to converge with the RP interviewer. The


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perceived changes in the accents of the adults were measured on an 11-point scale by two

judges who were not linguistically trained and naive to the experiment. Crucially,

however, the judgments of convergence and divergence came from instances of running

speech. This means that the perceptual judgments could have been made based on lexical

items, syntactic structure, or phonetic features. For example, one participant in the

experiment who was judged to have diverged from the RP speaker was heard conjugating

Welsh verbs. While this type of behavior is clearly divergent, it is distinct from a speaker

using a slightly different pronunciation to socially distance themself.

The goal of this paper is to attempt a replication of Bourhis and Giles looking at

phonetic convergence and divergence. When an individual wants to socially distance

themself from another speaker, do they do it phonetically? To answer this question, a

speech production task was designed using the voice of a male Australian as that of the

model talker (like the RP speaker in the Bourhis and Giles study) and New Zealand

participants. Australian and New Zealand Englishes (AuE and NZE) share many basic

dialect features (Bauer, Warren, Bardsley, Kennedy, & Major 2007, Cox & Palethorpe

2007), but there are several key differences in the front vowel monophthongs (Watson,

Harrington, & Evans 1998, Easton & Bauer 2000). Ongoing sound changes in NZE have

made the front vowel space particularly distinct in the two dialects (Maclagan & Hay

2007). In NZE, for instance, the vowels DRESS and TRAP are raising.1 The KIT vowel

in AuE is raising, while in NZE it is centralizing. Not all of these sound changes are

salient differences within the NZ community. Bayard (2000) states that KIT is the most

salient difference between NZE and AuE. Hay, Nolan, and Drager (2006) examined

NZE listeners’ sensitivity to the salient differences in KIT, TRAP, and DRESS in NZE

1 Following Wells (1982), lexical sets will be used to refer to the vowel categories in this paper.


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and AuE; they found equally strong results for KIT and TRAP, but NZE listeners did not

behave as though they were explicitly aware of the differences between DRESS in these

two dialects. It is important to be aware of the salient differences across the two dialects

as Trudgill (1981) argues that only socially salient variables are susceptible to

accommodation. Following this, in interactions between NZE and AuE speakers, we

predict that only TRAP and KIT would exhibit accommodation.

METHODOLOGY

In the following paragraphs I describe the speech production task used to explore

phonetic convergence and divergence in NZE. Following that, I provide the

methodology for the Implicit Association Task that measured each NZ participants’

inherent bias toward Australia. Participants’ scores on this task were used in the

statistical analysis described in the ANALYSIS & RESULTS section.

Speech Production Task

Participants (females = 34, males = 8) from the Victoria University of Wellington

community completed an auditory naming task. The task took approximately 30 minutes

and participants were compensated with a $10 book voucher. The auditory stimuli were

monosyllabic single word productions from a male talker who was born and raised in

Melbourne, Australia. Monophthongal stimuli were words taken from the lexical sets

KIT, DRESS, TRAP, BARN, STRUT, and THOUGHT. In addition to the target

monophthongs, participants were also presented with diphthongs involved in the

NEAR/SQUARE merger in New Zealand English (Hay, Warren, & Drager 2006). Only


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the results from the monophthongs are presented in this paper. The full list of

monophthongal words used in the task appears in the Appendix.

Participants were seated at a PC laptop and the experiment was presented using E-

Prime 2.0 Experimental Software (Schneider, Eschman, & Zuccolotto 2002). Auditory

stimuli were presented over AKG K271 headphones. Audio-recording was done directly

in E-prime using an M-Audio USB audio device with a head-mounted AKG C520

microphone positioned three inches from the participant’s mouth.

The task was designed as follows: Participants were randomly presented with

hVd words (hid, had, head, etc.) which they were to read aloud. In the next block,

participants were presented with the target word list which they were asked to produce

aloud. The words in the list were presented in a different random order for each

participant; this is referred to as the pre-task block. The purpose of the pre-task block is

to obtain a baseline production of how a participant produces each word. The following

block was the shadowing block where participants were exposed to the target word

productions from the Australian model talker over headphones. Words were randomly

presented twice through the course of the test block. Participants’ instructions for this

part of the task are to identify the word heard by saying it out loud. Finally, participants

did a post-task reading of the wordlist; this block was identical to the pre-task block,

except that the words were presented in a different random order. Finally, participants re-

read the hVd words again. The methodology of the auditory naming task follows that of

Goldinger (1998) and Namy, Nygaard, and Sauerteig (2002). In comparing pre-task and

test-block productions, we can see how NZ participants modify productions as a result of

exposure to the Australian model talker. In this task participants were assigned to one of


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two conditions. In the POSITIVE CONDITION, participants were presented with the

following text which was intended to make them view the talker and Australia as a whole

in a positive light:

The Australian talker you are about to hear was actually born in Auckland. At a

young age, however, he and his parents moved to Melbourne where he has lived

since. His grandparents and the rest of his extended family still live in New

Zealand, so he visits frequently. In fact, he is currently looking for employment

in New Zealand so that his children may live closer to their great-grandparents.

The other condition was a NEGATIVE CONDITION. The purpose of this condition was to

inspire negative feelings toward the talker and Australia.

The Australian talker you are about to hear was born in Sydney. Like many

Australians, he has strong negative opinions of New Zealand. For one, he thinks

that New Zealanders are rather stupid and that they lack culture. In addition, he

finds the entire population backwards and naïve. In his mind, New Zealand is

provincial and has a horrid cricket team. He never intends to visit New Zealand

because of these views.

In both conditions participants were exposed to a screen with the assigned text

immediately before beginning the test-block. After reading the Positive or Negative text


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participants pressed a button that took them to the test-block. Male and female

participants were evenly assigned to the two conditions.

Upon completion of the speech production task all participants took an Implicit

Association Task. This task is described in the next section.

Implicit Association Task

Traditionally in sociolinguistics, attitudes are elicited explicitly through surveys

or questionnaires. One goal of this project was to examine how implicit and

subconscious attitudinal measures may predict speech behavior. To this end, an Implicit

Association Task (IAT; Greenwald, McGhee, & Schwarz 1998) was used. The IAT is a

standard social psychology tool that uses reaction time in category classification to

measure implicit biases.

There are five blocks in this task. The first block is target-concept discrimination.

The targets AUSTRALIA and NEW ZEALAND were presented on opposite sides of the

monitor. A combination of Australian or New Zealand concepts – famous individuals,

maps, and images (flags, native scenery, sports emblems, etc.) – were then randomly

presented (e.g. an image of a kangaroo or an image of a kiwi) in the middle of the screen.

A participant’s task is to categorize the concepts as AUSTRALIA or NEW ZEALAND

as quickly as possible. The second block is associated attribute discrimination. Here, the

attributes good and bad are presented on opposite sides of the computer screen in place of

AUSTRALIA and NEW ZEALAND. Attribute words are presented randomly (e.g.

rainbow or cancer) in the middle of the screen. Participants categorize the words as

semantically good or bad words. The third block is a combined test block. Labels for the


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concept categories (AUSTRALIA vs. NZ) and word (good vs. bad) attributes are

presented at the top corners of the screen. In the center, either a concept or a word are

randomly presented and must be categorized. Participants are instructed to ignore the

target-concept when categorizing words and ignore the attributes when categorizing

concepts. Concepts that are words are presented in all capital letters and good/bad words

are presented in all lowercase letters to facilitate the process. Block 4 is just like Block 1,

except that the labels AUSTRALIA and NEW ZEALAND were presented on different

sides of the screen (so, if AUSTRALIA was on the right-side of the screen in Block 1, it

was on the left side in Block 4). Participants then categorized concepts as AUSTRALIA

or NEW ZEALAND as they did in Block 1. Block 5 is the reversed combined task; the

reversed order of the target-concepts (AUSTRALIA and NEW ZEALAND) are matched

up with the original order of good and bad such that if AUSTRALIA was originally

presented above good and NEW ZEALAND with bad, this pattern is reversed and

Australia is presented with bad and New Zealand with good. The experiment was

counterbalanced so that half of the participants were initially exposed to AUSTRALIA

paired with good and NEW ZEALAND paired with bad while the other half were first

presented with AUSTRALIA paired with bad and NEW ZEALAND paired with good.

Participants logged responses using assigned buttons on a computer keyboard.

Responses were collected automatically using E-prime. Participants’ scores were

calculated using the updated methods described in Greenwald, Nosek, and Banaji (2003).

After removing outliers, the mean reaction time was calculated for each participant based

on correct responses for each block. One standard deviation was also calculated for each

block. Then, each response error was replaced with the block mean and a 600 ms penalty.


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Means were then re-calculated for each block and the difference between these two

blocks was computed. Finally, to get the IAT score, the difference was divided by the

standard deviation previously calculated. These values were used as predictors in the

statistical model described below.

ANALYSIS & RESULTS

Analysis

Vowels from the participant productions were hand-marked. A Praat (Boersma &

Weenink 2005) script calculated the average first and second formant over the middle

50% of the vowel. Obvious outliers were hand-corrected. In order to minimize

physiological differences between participants so that the analysis can focus on speech

production differences in dialect and style, it is necessary to normalize the vowel

formants. As per Adank, Smits, and van Hout (2004), the Lobanov normalization

method was used (Lobanov 1971).

With the normalized formant values, the Euclidean distance between each NZ

word production and the same word from the Australian model talker was calculated.

Euclidean distance is a way of measuring distance; essentially, it measures distance as the

crow flies. This means that for each word from each block for each participant there is a

single distance measure. To understand how NZ participants’ productions changed as a

result of exposure to the model talker, the distance for each word from the test-block and

post-task blocks were subtracted from the pre-task block distances, creating a difference

in distance metric. This difference in distance measure is indicative of how much a

participant modified their phonetic distance to the speech of the model talker. A negative


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value indicates the distance between the NZ participant and the Australian model has

shrunk, indicating convergence. A positive value signals the phonetic distance has

grown; this would mean there was divergence.

Results from the Shadowed Productions

A stepwise hierarchical linear regression analysis using only the data from the

block of shadowed productions was run. With this type of analysis all potential main

effects and interactions are evaluated against one another. The model automatically

selects the predictor variables that account for larger proportions of variance in the data.

The difference in distance metric was used as the dependent variable while vowel

category (BARN, DRESS, KIT, TRAP, STRUT, THOUGHT), participant gender (male

or female), experimental condition (Positive or Negative), participant age, word

frequency, and IAT score were entered as independent predictors. Word frequency was

determined by logarithmic frequency counts in CELEX (Baayen, Piepenbrock, & van

Rijn 1993). The variables that were used by the model are shown in Table 1. The model

selected Vowel, participants’ IAT score, word frequency, and the Vowel by IAT

interactions as predictors in the model [F (12, 2037) = 6.2, p < 0.001]. The negative

intercept in Table 1 indicates that on average participants converged with the model

talker; the difference in distance was less in the shadowed tokens than in the pre-task

tokens.


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β Standard Error t-value

Intercept -0.179455 0.025179 -7.127*** DRESS -0.049902 0.027372 -1.823 KIT 0.040225 0.027291 1.474 STRUT 0.063596 0.024102 2.639** THOUGHT 0.071848 0.025304 2.839** TRAP 0.056860 0.023731 2.396* IAT 0.089247 0.030549 2.921** word frequency 0.045078 0.011709 3.850*** DRESS * IAT 0.060010 0.049759 1.206 KIT * IAT -0.129701 0.049571 -2.616** STRUT * IAT -0.086119 0.043395 -1.985* THOUGHT * IAT 0.007461 0.045661 0.163 TRAP * IAT -0.059030 0.043033 -1.372

Table 1. Results of a stepwise hierarchical linear regression used to predict difference in distance values from the shadowed tokens. Symbols following the t-values indicate the associated p-value: ‘***’ p < 0.001, ‘**’ p < 0.01, and ‘*’ p < 0.05.

Participants’ IAT score and word frequency have positive β coefficients in Table

1. This means that the difference in distance value increased as these values increased.

For word frequency, this is in accordance with previous work; lower frequency words

exhibit more imitation than high frequency words (Goldinger 1998). In terms of the IAT

score, the positive coefficient demonstrates that participants who scored as pro-Australian

were more likely to accommodate toward the vowels of the Australian model talker. The

Condition to which participants were assigned did not show up in the regression model.

In addition, post-hoc analysis found no reliable effect of the Positive or Negative

Conditions on difference in distance values. Figure 1 presents participants’ IAT scores

plotted against their average difference in distance value. Again, these results show that

participants who scored as pro-Australian in the IAT were significantly more likely to

spontaneously accommodate toward the vowels of the Australian talker [t (40) = 2.2, p <

0.05, Pearson’s R = 0.32]. In Figure 1, each “positive” or “negative” data point

represents one participant and indicates which Condition the participant was assigned to.


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This method of presenting the data also reveals the trend – albeit insignificant – that

participants in the Negative Condition scored as more pro-New Zealand in the IAT than

those participants randomly assigned to the Positive Condition.

-1.5 -1.0 -0.5 0.0 0.5 1.0

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Avera

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Figure 1: Each participant’s averaged difference in distance plotted against their IAT score. The Difference in distance measure on the y-axis indicates the amount of phonetic accommodation. A negative value demonstrates phonetic convergence while a positive value demonstrates phonetic divergence. On the x-axis, a negative IAT score indicates a pro-Australian bias and a positive score a pro-New Zealand bias. The negatively skewed regression line in the figure demonstrates that a participant was more likely to phonetically accommodate to the Australian talker when they scored with a pro-Australian bias on the IAT.


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-0.1

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barn dress kit strut thought trap

Figure 2. Amount of phonetic accommodation to the vowels averaged across all participants in both the Positive and Negative Conditions. The difference in distance measure on the y-axis indicates the amount of phonetic accommodation. A negative value demonstrates phonetic convergence while a positive value demonstrates phonetic divergence. The dashed-line marks the cut-off point for accommodation; all data below the line is indicative of phonetic convergence. The error bars represent 95% confidence intervals.

While the overall pattern showed convergence, participants did not accommodate

toward all vowels to the same extent. Figure 2 presents difference in distance values for

each vowel averaged across all participants. Any value below zero – marked by the


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dashed line in the figure – indicates some degree of phonetic accommodation toward the

vowels of the AuE talker. Negative difference in distance values for all vowels show that

there was convergence toward the model talker and no patterns of divergence in the data

set as a whole. Looking at Figure 2, it is also apparent that participants respond to

vowels in a non-uniform manner. For example, it seems that DRESS elicited the most

accommodation. Indeed, post-hoc analyses found that the DRESS vowel was imitated

more than STRUT (p > 0.05), THOUGHT (p > 0.001), and TRAP (p > 0.01). BARN

was also imitated more than THOUGHT (p > 0.05).2

Figures 3 and 4 plot the averaged normalized vowel formants for female and male

participants, respectively. In both figures the words printed in the larger font represent

the averaged vowel formants for the lexical sets from the Australian talker. The smaller

words in black are averaged from participants’ pre-task productions. The smaller words

in black italics are the shadowed productions and those in small gray italics are

productions from the post-task block. These figures depict the direction of the

accommodation. They also demonstrate trends toward different behaviors between men

and women that did not come out as significant in the model, but are interesting

nevertheless. For female participants (Figure 3), all vowels in the test block shifted in the

direction of the Australian talker. Male participants (Figure 4), however, display this

same pattern of drift toward the Australian talker for DRESS, BARN, STRUT, and

THOUGHT, but demonstrate divergence or maintenance for KIT and TRAP. TRAP in

particular seems to be diverging from that of the AuE model speaker in this figure. The

lowering of the first formant in the test block is a shift in the direction of the ongoing

sound change in NZE (Maclagan & Hay 2007). This data is only based on the speech of

2 Post-hoc analyses were Tukey’s Honest Significant Difference tests.


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eight males, but it is important and interesting to observe the pattern for TRAP and KIT

as these are the vowel sets documented as the most salient distinguishers of NZ and Au

Englishes.

1 0 -1 -2

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F2 (normalized)

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alized)

barn

dress

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trap

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dress

kit

strut

thought

trap

barn

dress

kit

strut

thought

trap

barn

dress

kit

strut

thought

trap

Figure 3: Formant plot displaying phonetic accommodation for the female participants. The Australian model talker’s mean vowels are plotted in the larger black typeface. Female participants’ pre-task productions are in a small black font, shadowed productions are in black italics, and post-task productions and in gray italics. The shadowed productions move in the direction of the model talker’s tokens and the post-task productions generally lie somewhere between the pre-task and shadowed productions.


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1 0 -1 -2

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barn

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dress

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trap

Figure 4. Formant plot displaying phonetic accommodation for the male participants. The Australian model talker’s mean vowels are plotted in the larger black typeface. Male participants’ pre-task productions are in a small black font, shadowed productions are in black italics, and post-task productions and in gray italics. The shadowed productions move in the direction of the model talker’s tokens and the post-task productions generally lie somewhere between the pre-task and shadowed productions.


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Results from the Post-task Productions

For the post-task productions, another stepwise hierarchical linear regression

analysis was used. Again, the difference in distance metric was used as the dependent

variable while vowel category (BARN, DRESS, KIT, TRAP, STRUT, THOUGHT),

participant gender (male or female), experimental condition (Positive or Negative),

participant age, word frequency, and IAT score were entered as the predictor variables.

The variables chosen by the model are summarized in Table 2. The model selected IAT

scores, Experiment Condition (with Positive as the model’s default reference variable),

word frequency, and participant age [F (4, 1039) = 4.3, p < 0.05]. Like what was found

for the shadowed productions, the negative intercept indicates that overall the difference

in distance values in the post-task block were smaller than those in the pre-task. After

exposure to the Australian model talker ceased, NZ participants retained the more

Australian-like vowels they had acquired during the shadowing block.

β Standard Error t-value

Intercept -0.152895 0.048217 -3.171** IAT 0.050129 0.017045 2.941** Positive Condition 0.032860 0.018559 1.771 word frequency 0.021871 0.013836 1.581 participant age 0.002761 0.001794 1.539

Table 2. Results of a stepwise hierarchical linear regression used to predict difference in distance values from the post-task tokens. Symbols following the t-values indicate the associated p-value: ‘***’ p < 0.001, ‘**’ p < 0.01, and ‘*’ p < 0.05.

Only IAT score contributed significantly to this model. The positive β coefficient

for IAT in Table 2 means that participants who scored as pro-Australian were more likely

to continue their accommodative vowel behavior into the post-task reading. The

correlation between a participant’s average difference in distance and their IAT score was


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slightly stronger with the post-task productions than it had been with the shadowed

productions [t (40) = 2.5, p < 0.05, Pearson’s R = 0.37]. While Positive Condition was

used as a predictor in the regression model, its contribution was not significant.

Moreover, post-hoc analysis found no difference in the amount of accommodation across

the Positive and Negative Conditions in the post-task productions. Again, like in the

shadowed productions, lower frequency words elicited more accommodation (Goldinger

1998). Participant age was used in the model, but it did not make a significant

contribution in account for variance in the data. Still, it is interesting that this predictor

has a positive β coefficient. This indicates a trend that younger participants

accommodated more than older participants. There were no differences in degree of

imitation between vowels in the post-task productions. The general pattern of

accommodative behavior can be seen in Figure 3 and 4. The post-task productions (small

gray italics) are intermediate between the pre-task productions (small black regular font)

and the shadowed productions (small black italics).

CONCLUSION

In this study New Zealand participants completed an auditory word-naming

speech production task where the model voice was a speaker of Australian English. In an

attempt to replicate Bourhis and Giles (1977), one group of NZ participants was insulted

(the Negative Condition) and the other was flattered (the Positive Condition) through a

story about the AuE speaker. The first and second formants of monosyllabic words

containing the vowels KIT, DRESS, TRAP, BARN, STRUT, and THOUGHT were

acoustically analyzed for acoustic convergence and divergence. This was determined by


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calculating the phonetic distance of responses in the shadowed block and post-task block

to NZ productions made in the pre-task block prior to any knowledge that the task

involved an Australian. In addition to completing a speech production task, NZ

participants took an IAT designed to explore implicit biases to Australia and New

Zealand. Stepwise hierarchical linear regression models were used to analyze the tokens

from the shadowed block and the post-task block. This type of model automatically

selects predictors that best account for the data and ignores others. For the model using

the shadowed productions, each of the vowels were selected by the model along with

word frequency and participants’ IAT scores. The results were vowel-specific such that

not all vowels were imitated to the same extent. In terms of word frequency, participants

accommodated to lower frequency words more than higher frequency words (Goldinger

1998). For IAT scores, participants who scored as pro-Australian on the task were more

likely to accommodate to the AuE model talker than those who scored with a pro-New

Zealand bias. Crucially, scores on this task were selected by the model as a predictor

while assignment to the Positive or Negative Condition was not. For the post-task

productions, IAT scores and word frequency had the similar effect as they did for the

shadowed model. Only IAT scores contributed significantly to this second model; pro-

Australian IAT scores resulted in more accommodation. In the post-task model, lower

word frequency, younger participants, and assignment to the Positive Condition all

tended toward more accommodation, but not significantly so.

Bourhis and Giles (1977) found convergence in a group of Welsh participants

when they were meant to feel solidarity with a speaker of RP and divergence in a group

who disagreed with a view expressed by the RP speaker. In general in this study, NZ


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participants converged on the spectral characteristics of an AuE speaker regardless of the

feelings presumably incited by the task design. Differences in accommodation were

found, however, based on participants’ pre-existing sentiments toward Australia. The

more positive participants’ feelings were toward Australia, the more likely they were to

converge on the model speaker’s vowels. If IAT scores are a means of participants self-

organizing themselves into “positive” and “negative” groups (like those of Bourhis &

Giles), the finding is similar to that of Bourhis & Giles. Positive feelings lead to greater

likelihood to accommodate, but, crucially, the key result in this experiment is that all

participants accommodated. Social biases inhibited the degree of accommodation, but

the default behavior was for vowel convergence.

The vowel-specific findings also merit discussion. The DRESS vowel was

imitated to a greater extent than all other vowels used in the task. While this vowel is

produced very differently in the two dialects (compare NZE [dɹis], AuE [dɹes̞]), it is not

considered a particularly salient difference (Hay, Nolan, & Drager 2006). In this study,

the most salient dialect differences were not the most imitated; this finding is contra

Trudgill (1981). The vowel-specific results seem to contradict Trudgill (2008) to some

extent. Trudgill’s recent work claims accommodation is automatic and that social

identities play no role in whether it happens, but that social ties are fostered as a result of

accommodation. In this project, it was found that vocalic convergence is automatic in the

sense that participants are not aware they are doing it, but it is not automatic in the sense

of being a process that happens at all times. Some vowels are targeted more than others

(see also Babel 2009). Moreover, with respect to social identities, implicit socio-

cognitive biases about how a participant feels about a speaker determine the extent of


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accommodation. These biases are automatic (Djiksterhuis & Bargh 2001), but, crucially,

exist prior to the interaction that elicits convergent speech behavior. This result leads to a

nuanced view reminiscent to that of Trudgill (2008): speakers of language cannot help

accommodating, but group identity attitudes modulate this automatic process.


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APPENDIX

Word list for speech production task.

hint bid hit bet bed

beg ban bad bat back

bag bore bored born bought

bug bud buck but bun

path bark laugh BARN dance

ACKNOWLEDGEMENTS

This project has been supported by funds from the National Science Foundation EAPSI

program and the Abigail Hodgen Publication Fund at University of California, Berkeley.

Thanks to Dr. Paul Warren for hosting me at Victoria University of Wellington and

allowing me to conduct this research. This work has been improved by Keith Johnson

and Andrew Garrett. Many thanks to the participants who volunteered for this project.

All errors are, of course, my own.


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REFERENCES

Adank, Patti, Roel Smits, & Roeland van Hout. (2004). A comparison of vowel

normalization procedures for language variation research. Journal of the

Acoustical Society of America, 116:3, 99-107.

Baayen, R.H., R. Piepenbrock, and H. van Rijn. (1993). The CELEX lexical database

(CDROM). Linguistic Data Consortium, University of Pennsylvania.

Babel, Molly. (2009). Phonetic and Social Selectivity in Speech Accommodation.

Doctoral Dissertation, University of California, Berkeley.

Bayard, Donn. (2000). The cultural cringe revisited: changes through time in Kiwi

attitudes toward accents. In Allan Bell & Koenraad Kuiper (eds.) New Zealand

English, 297-324. John Benjamins Publishing Company.

Bauer, Laurie, Paul Warren, Dianne Bardsley, Marianna Kennedy, & George Major.

(2007). An illustration of the IPA: New Zealand English. Journal of the

International Phonetics Association, 37(1). 97–102.

Bauer, Laurie. (2008). A question of identity: A response to Trudgill. Language in

Society, 37:272–273.

Boersma, Paul & David Weenink. (2005). Praat: doing phonetics by computer (version

4.3.29) [computer program], from http://www.praat.org/. Institute of Phonetic

Sciences, Amsterdam.

Bourhis, Richard Y. & Howard Giles. (1977). The language of intergroup distinctiveness.

In Howard Giles (ed.) Language, ethnicity, and intergroup relations, 119–136.

Chambers, Jack. (1992). Dialect acquisition. Language 68:673–705.


158

Coupland, Nikolaus. (2008). The delicate constitution of identity in face-to-face

accommodation: A response to Trudgill. Language in Society, 37:267–270.

Cox, Felicity & Sallyanne Palethorpe (2007). An illustration of the IPA: Australian

English. Journal of the International Phonetic Association, 37, 341-350.

Delvaux, Véronique & Alain Soquet. (2007). The influence of ambient speech on adult

speech productions through unintentional imitation. Phonetica, 64:145–173.

Dijksterhuis, Ap & John A. Bargh. (2001). The perception-behavior expressway:

Automatic effects of social perception on social behavior. In Mark Zanna (ed),

Advances in Experimental Social Psychology, 1–40.

Easton, Anita & Bauer, Laurie. (2000). An acoustic study of the vowels of New

Zealand English. Australian Journal of Linguistics 20 (2), 93-117.

Evans, Bronwen G. & Paul Iverson. (2007). Plasticity in vowel perception and

production: A study of accent change in young adults. Journal of the Acoustical

Society of America, 121:3814–3826.

Holmes, Janet & Paul Kerswill. (2008). Contact is not enough: A response to Trudgill.

Language in Society, 37:273–277.

Giles, Howard. (1973). Accent mobility: a model and some data. Anthropological

Linguistics, 15:87–105.

Giles, Howard, & Nikolas Coupland. (1991). Language: Contexts and consequences.

Milton Keynes: Open University Press.

Giles, Howard, Nikolas Coupland, & Justine Coupland. (1991). Accommodation theory:

Communication, context, and consequence. In Howard Giles, Justine Coupland,

and Nikolas Coupland (eds.) Contexts of Accommodation, 1–68.


159

Goldinger, Stephen D. (1998). Echoes of echoes? An episodic theory of lexical access.

Psychological Review, 105:251–279.

Goldinger, Stephen D. & Tamiko Azuma. (2004). Episodic memory in printed word

naming. Psychonomic Bulletin & Review, 11:716–722.

Greenwald, Anthony G., Debbie E. McGhee, & Jordan L. K. Schwartz. (1998).

Measuring individual differences in implicit cognition: The implicit association

test. Journal of Personality and Social Psychology, 74:1464–1480.

Greenwald, Anthony G., Brian A. Nosek, & Mahzarin R. Banaji. (2003). Understanding

and using the Implicit Association Test: I. An improved scoring algorithm.

Journal of Personality and Social Psychology, 85:197–216.

Hay, Jennifer, Aaron Nolan, & Katie Drager. (2006). From fush to feesh: Exemplar

priming in speech perception. The Linguistic Review, 23, 351-379.

Hay, Jennifer, Paul Warren, & Katie Drager. (2006). Factors influencing speech

perception in the context of a merger-in-progress. Journal of Phonetics, 34, 458-

484.

Lobanov, B. (1971) Classification of Russian vowels spoken by different listeners.

Journal of the Acoustical Society of America, 49, 606-08.

Maclagan, Margaret & Jennifer Hay. (2007). Getting fed up with our feet: Contrast

Maintenance and the New Zealand English “short” front vowel shift. Language

Variation and Change, 19, 1-25.

Mufwene, Salikoko S. (2008). Colonization, population contacts, and the emergence of

new language varieties: A response to Trudgill. Language in Society, 37:254–

258.


160

Munro, Murray J., Tracey M. Derwing, & James E. Flege. (1999). Canadians in

Alabama: A perceptual study of dialect acquisition in adults. Journal of Phonetics

27:385–403

Namy, Laura L., Lynne C. Nygaard, & Denise Sauerteig. (2002). Gender differences in

vocal accommodation: the role of perception. Journal of Language and Social

Psychology, 21:422–432.

Nielsen, Kuniko. (2008). The specificity of allophonic variability and its implications for

accounts of speech perception. Doctoral Dissertation, University of California,

Los Angeles.

Pardo, Jennifer S. (2006). On phonetic convergence during conversational interaction.

Journal of the Acoustical Society of America, 119:2382–2393.

Payne, Arvilla C. (1980). Factors controlling the acquisition of the Philadelphia dialect

by out-of-state children. In William Labov (ed.) Locating language in time and

space, 179–218. Academic Press: New York.

Pickering, Martin J. & Simon Garrod. (2004). Toward a mechanistic psychology of

dialogue. Behavioral and Brain Sciences, 27:169–226.

Schneider, Edgar W. (2008). Accommodation versus identity?: A response to Trudgill.

Language in Society, 37:262–267.

Schneider, Walter, Amy Eschman, & Anthony Zuccolotto. (2002). E-Prime: User’s

Guide, version 1.0. Psychology Software Tools.

Shepard, Carolyn A., Howard Giles, & Beth A. LePoire. (2001). Communication

accommodation theory. In W. Peter Robinson and Howard Giles (eds.) The New

Handbook of Language and Social Psychology, 33–56. John Wiley & Sons Ltd.


161

Shockley, Kevin, Laura Sabadini, & Carol A. Fowler. (2004). Imitation in shadowing

words. Perception & Psychophysics, 66:422–429.

Trudgill, Peter. (1981). Linguistic accommodation: Sociolinguistic observations on a

sociopsychological theory. In Roberta Hendrick, Carrie Masek, and Mary Frances

Miller (eds.), Papers from the Parasession on Language and Behavior, 218–237.

Chicago: Chicago Linguistics Society.

Trudgill, Peter. (1986). Dialects in contact. Blackwell Publishing.

Trudgill, Peter. (2008). Colonial dialect contact in the history of European languages: On

the irrelevance of identity in new-dialect formation. Language in Society, 37:241–

280.

Tuten, Donald N. (2008). Identity formation and accommodation: Sequential and

simultaneous relations. Language in Society, 37:259–262.

Watson, Catherine, Jonathan Harrington & Zoe Evans. (1998). An acoustic

comparison between New Zealand and Australian English vowels. Australian

Journal of Linguistics, 18, 185–207.

Wells, J. C. (1982). Accents of English. Cambridge University Press, Cambridge.


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